Non-invasive imaging technologies allow images of the internal structures of a patient or object to be obtained without performing an invasive procedure on the patient or object. In particular, technologies such as computed tomography (CT) use various physical principles, such as the differential transmission of x-rays through the target volume, to acquire image data and to construct tomographic images (e.g., three-dimensional representations of the interior or the human body or of other imaged structures).
New post-processing techniques can substantially improve the functionality of an imaging system as well as the accuracy of clinical diagnoses. For example, modern deep learning techniques may allow lesions to be accurately detected in tomographic images with a lower image quality, thereby enabling a reduction in radiation dose (and thus a potential reduction in image quality) without sacrificing the diagnostic effectiveness of the imaging system. One notable feature of deep learning algorithms is the ability for the algorithm to improve over time as it is trained on additional imaging data acquired by the imaging system. However, it is difficult to leverage these improvements for other imaging systems, as training the deep learning algorithm typically requires access to the raw imaging data, which potentially includes sensitive patient information.